Background

Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field’s complex background, rice panicle segmentation in the field is a very large challenge.

Results

In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online.

Conclusions

In conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.

Context

Landscape ecology has traditionally been taught through theoretical classes or computer labs. On the other hand, field labs have been generally less used as a way of teaching landscape ecology concepts.

Objectives

We show that field labs with an inquiry-based approach, where students are involved in the investigation, are feasible for training students in landscape ecology. We evaluated how common field labs are in landscape ecology courses, and also their contribution for student learning.

Methods

We evaluated whether field labs are used in landscape ecology courses by scanning available syllabi. We also used outcomes from a course offered in Brazil to show how field labs can be integrated into landscape ecology courses.

Results

Only 18.2 % of the 44 syllabi we found had field labs. The case study developed in Brazil showed that field labs allowed students to develop important skills, including the ability to design field studies, choose appropriate scales of analysis, detect ecological patterns, and judge multiple hypotheses.

Conclusions

Field labs are still uncommon in landscape ecology courses, but they can be a useful tool to teach landscape ecology concepts and to help students developing the necessary skills to do research. We offer recommendations regarding how to incorporate field labs in landscape ecology courses.

Background

The number of cultivated wheat seedlings per unit area allows calculation of plant density. Wheat seedling density provides emergence data and this is useful for improving crop management. The number of wheat seedlings is typically determined by visual counts but this is time-consuming and laborious.

Results

We obtained field digital images of 1st to 3rd leaf stage wheat seedlings. The seedlings were extracted using an image analysis technique that calculated the coverage degree of the seedlings and the number of angular points of overlapping leaves. The wheat seedling quantity estimation model was constructed using multivariate regression analysis. The model parameters included coverage degree, number of angular points, variety coefficient, and leaf age. Introduction of the number of angular points increased the accuracy of the single coverage degree model. The R² value was consistently?>?0.95 when the model was applied to different varieties, indicating that the model was adaptable for different varieties. As the leaf stage or density increased, the accuracy of the model declined, but the minimum R² remained?>?0.87, indicating good adaptability of the model to seedlings with different leaf ages and densities.

Conclusions

This method is an effective means for counting wheat seedlings in the 1st to the 3rd leaf stages.